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\title{Appendix B: Belt and Road and UNGA Voting}
\usepackage{etoolbox}
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\subtitle{Codebook}
\author{}
\date{\vspace{-2.5em}}

\begin{document}
\maketitle

{
\setcounter{tocdepth}{3}
\tableofcontents
}
\setstretch{1.15}
\hypertarget{introduction}{%
\section{Introduction}\label{introduction}}

\hypertarget{data-sources}{%
\subsection{Data sources}\label{data-sources}}

The data collected for the paper: XYZ originates from four main sources.

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  BRI data
\item
  Polity4 Project
\item
  World Bank data
\item
  Fariss Human Rights Indicator
\end{enumerate}

\hypertarget{datasets}{%
\subsection{Datasets}\label{datasets}}

We compile three datasets. As we are interested in the changes compared
to China, the US, and Russia, we compile a data set for each of the
countries. The following codebook displays the mean and standard
deviation of each variable for each comparison country. These differ
especially for those variables where the distance to the value for
China, the US, or Russia is calculated. We also present histograms of
each variable displaying the distribution of each variable.

\newpage

\hypertarget{codebook}{%
\section{Codebook}\label{codebook}}

\hypertarget{dyad_id}{%
\subsection{dyad\_id}\label{dyad_id}}

Numeric ID of country pairs. For example: Afghanistan - Albania is ID 1.

\hypertarget{ctry1}{%
\subsection{ctry1}\label{ctry1}}

First country in Dyad ID, country name.

\hypertarget{ctry2}{%
\subsection{ctry2}\label{ctry2}}

Second country in Dyad ID, country name.

\hypertarget{cown1}{%
\subsection{cown1}\label{cown1}}

First country in Dyad ID, correlates of war numeric identifier.

\hypertarget{cown2}{%
\subsection{cown2}\label{cown2}}

First country in Dyad ID, correlates of war numeric identifier.

\hypertarget{year}{%
\subsection{year}\label{year}}

Numeric year

\hypertarget{ctry1_mou}{%
\subsection{ctry1\_mou}\label{ctry1_mou}}

Date of MoU signature for country 1 in Dyad

\hypertarget{ctry2_mou}{%
\subsection{ctry2\_mou}\label{ctry2_mou}}

Date of MoU signature for country 2 in Dyad

\hypertarget{ctry1_neg}{%
\subsection{ctry1\_neg}\label{ctry1_neg}}

Date of MoU-negotiation start for country 1 in Dyad

\hypertarget{ctry2_neg}{%
\subsection{ctry2\_neg}\label{ctry2_neg}}

Date of MoU-negotiation start for country 2 in Dyad

\hypertarget{ctry1_bri_start}{%
\subsection{ctry1\_bri\_start}\label{ctry1_bri_start}}

Date of construction start for country 1 in Dyad

\hypertarget{ctry2_bri_start}{%
\subsection{ctry2\_bri\_start}\label{ctry2_bri_start}}

Date of construction start for country 2 in Dyad

\newpage

\hypertarget{ctry1_polity2}{%
\subsection{ctry1\_polity2}\label{ctry1_polity2}}

Country 1 Polity2 Score. ``Revised Combined Polity Score: This variable
is a modified version of the POLITY variable added in order to
facilitate the use of the POLITY regime measure in time-series
analyses.'' (from Polity codebook)

\includegraphics{data_final_files/figure-latex/unnamed-chunk-4-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & -7.00 & 0.00\\
United States & 9.79 & 0.61\\
Russia & 4.30 & 1.11\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry2_polity2}{%
\subsection{ctry2\_polity2}\label{ctry2_polity2}}

Country 2 Polity2 Score. ``Revised Combined Polity Score: This variable
is a modified version of the POLITY variable added in order to
facilitate the use of the POLITY regime measure in time-series
analyses.'' (from Polity codebook)

These are the polity scores of China, Russia, and the US respectively.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-6-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 3.34 & 6.52\\
United States & 3.23 & 6.55\\
Russia & 3.27 & 6.57\\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Country & Mean & Standard Deviation\\
\midrule
China & -7.00 & 0.00\\
United States & 9.79 & 0.61\\
Russia & 4.30 & 1.11\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{polity2_dist}{%
\subsection{polity2\_dist}\label{polity2_dist}}

Absolute distance between the polity2 scores of country 1 and country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-9-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 10.62 & 6.05\\
United States & 6.67 & 6.50\\
Russia & 5.56 & 3.61\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry1_gdp}{%
\subsection{ctry1\_gdp}\label{ctry1_gdp}}

Gross Domestic Product in US Dollars (2015), taking from World Bank, for
country 1.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-11-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 290295805807 & 1.195000e+12\\
United States & 236929569621 & 7.499884e+11\\
Russia & 311983578578 & 1.281712e+12\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry2_gdp}{%
\subsection{ctry2\_gdp}\label{ctry2_gdp}}

Gross Domestic Product in US Dollars (2015), taking from World Bank, for
country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-13-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 4.301968e+12 & 4.264167e+12\\
United States & 1.246813e+13 & 4.313788e+12\\
Russia & 9.664562e+11 & 6.671212e+11\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{gdp_dist}{%
\subsection{gdp\_dist}\label{gdp_dist}}

Absolute difference between gdp of country 1 and country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-15-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 4.206562e+12 & 4.184442e+12\\
United States & 1.230668e+13 & 4.248736e+12\\
Russia & 9.824891e+11 & 1.187697e+12\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry1_gdp_cap}{%
\subsection{ctry1\_gdp\_cap}\label{ctry1_gdp_cap}}

Gross Domestic Product per Capita in US Dollars (2015), taking from
World Bank, for country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-17-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 9623.85 & 15699.96\\
United States & 9371.66 & 15464.83\\
Russia & 9598.39 & 15701.24\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry2_gdp_cap}{%
\subsection{ctry2\_gdp\_cap}\label{ctry2_gdp_cap}}

Gross Domestic Product per capita in US Dollars (2015), taking from
World Bank, for country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-19-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 3195.73 & 3071.92\\
United States & 41814.59 & 11516.37\\
Russia & 6670.78 & 4649.70\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{gdp_cap_dist}{%
\subsection{gdp\_cap\_dist}\label{gdp_cap_dist}}

Absolute difference ind GDP per capita between country 1 and country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-21-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 8586.40 & 14277.94\\
United States & 33947.77 & 14487.89\\
Russia & 9299.51 & 12607.42\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry1_gdp_change}{%
\subsection{ctry1\_gdp\_change}\label{ctry1_gdp_change}}

Percentage change in GDP for country 1.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-23-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 3.71 & 6.59\\
United States & 3.75 & 6.61\\
Russia & 3.76 & 6.59\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry2_gdp_change}{%
\subsection{ctry2\_gdp\_change}\label{ctry2_gdp_change}}

Percentage change in GDP for country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-25-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 9.48 & 2.41\\
United States & 2.49 & 1.54\\
Russia & 0.86 & 6.31\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{gdp_change_dist}{%
\subsection{gdp\_change\_dist}\label{gdp_change_dist}}

Absolute distance in percentage change in GDP between country 1 and
country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-27-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 6.59 & 6.17\\
United States & 3.81 & 5.62\\
Russia & 5.81 & 6.59\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry1_fariss}{%
\subsection{ctry1\_fariss}\label{ctry1_fariss}}

the posterior mean of the new latent variable for Country 1 described in
``Fariss, Christopher; Michael Kenwick; Kevin Reuning, 2020,''Latent
Human Rights Protection Scores Version 4``,
\url{https://doi.org/10.7910/DVN/RQ85GK}, Harvard Dataverse, V2,
UNF:6:QPg88sybNJyuljPYph2OXQ== {[}fileUNF{]}''

\includegraphics{data_final_files/figure-latex/unnamed-chunk-29-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 0.35 & 1.44\\
United States & 0.34 & 1.45\\
Russia & 0.35 & 1.44\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{ctry2_fariss}{%
\subsection{ctry2\_fariss}\label{ctry2_fariss}}

the posterior mean of the new latent variable for Country 2 described in
``Fariss, Christopher; Michael Kenwick; Kevin Reuning, 2020,''Latent
Human Rights Protection Scores Version 4``,
\url{https://doi.org/10.7910/DVN/RQ85GK}, Harvard Dataverse, V2,
UNF:6:QPg88sybNJyuljPYph2OXQ== {[}fileUNF{]}''

\includegraphics{data_final_files/figure-latex/unnamed-chunk-31-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & -1.27 & 0.14\\
United States & 0.76 & 0.57\\
Russia & -1.43 & 0.37\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{fariss_dist}{%
\subsection{fariss\_dist}\label{fariss_dist}}

Distance in Fariss HR scores for country 1 and country 2.

\includegraphics{data_final_files/figure-latex/unnamed-chunk-33-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 1.75 & 1.29\\
United States & 1.37 & 0.98\\
Russia & 1.88 & 1.34\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{idealpointdistance}{%
\subsection{IdealPointDistance}\label{idealpointdistance}}

Voting similarity index between ccode1 and ccode2 in a given sesson --
computed using 3 category vote data (1 = ``yes'' or approval for an
issue; 2 = abstain, 3 = ``no'' or disapproval for an issue.) -
Abstention is counted as half-agreement with a yes or no vote. (from:
Erik Voeten ``Data and Analyses of Voting in the UN General Assembly''
Routledge Handbook of International Organization, edited by Bob Reinalda
(published May 27, 2013). Available at SSRN:
\url{http://ssrn.com/abstract=2111149})

\includegraphics{data_final_files/figure-latex/unnamed-chunk-35-1.pdf}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
Comparrison Country & Mean & Standard Deviation\\
\midrule
China & 0.87 & 0.78\\
United States & 2.92 & 0.88\\
Russia & 0.89 & 0.56\\
\bottomrule
\end{tabular}
\end{table}

\newpage

\hypertarget{identifying-voting-communities}{%
\section{Identifying voting
communities}\label{identifying-voting-communities}}

\hypertarget{distances-between-countries}{%
\subsection{Distances between
countries}\label{distances-between-countries}}

We create a network based on the average distance between UNGA
Idealponts over a ten year period, prior to the treatment, i.e.~the
accession to the BRI. The idea is, that we need to identify voting blocs
to accurately identify the sensitive changes in ideal points. To do so,
we calcualte the average distance and select those countries that are
within the 5 most proximate percentile to the country under
consideration.

To give an example, we calculate the average distance of each country to
Hungary and select the countries that are within the top 5th percentile.

\begin{table}

\caption{\label{tab:unnamed-chunk-44}Table 1: Most proximate countries to Hungary}
\centering
\fontsize{8}{10}\selectfont
\begin{tabular}[t]{llrr}
\toprule
Country 1 &  Country 2 & Average Distance & Fifth percentile\\
\midrule
Hungary & Bulgaria & 0.1213880 & 0.1321953\\
Hungary & Denmark & 0.0849741 & 0.1321953\\
Hungary & Finland & 0.0712913 & 0.1321953\\
Hungary & Iceland & 0.0914005 & 0.1321953\\
Hungary & Norway & 0.0984189 & 0.1321953\\
\addlinespace
Hungary & Poland & 0.0861380 & 0.1321953\\
Hungary & Romania & 0.1040041 & 0.1321953\\
\bottomrule
\end{tabular}
\end{table}

We repeat this process for each country in the data. The distances are
distributed as follows:

\begin{figure}

{\centering \includegraphics[width=0.75\linewidth]{../analysis/graphs/network/distribution-of-distances} 

}

\caption{Distibution of average distances (10 year period 2004-2013)}\label{fig:unnamed-chunk-45}
\end{figure}

It should be noted, however, that we limit the average distance to
\textless{} 0.25. Larger distances may be included in the data, simply
because there are countries that vote very dissimilarly to all other
states. Distances within 1/4 of a point seem reasonable, ultimately
though, this threshold ist arbitrary.

\hypertarget{networks}{%
\subsection{Networks}\label{networks}}

Based on the above-calcualted distances we create a network of voting
blocs in the UNGA, based on their ideal point distances between 2004 and
2013.

\begin{verbatim}
FALSE pdf 
FALSE   2
\end{verbatim}

We first plot a full network, including all countries. It displays,
however a large cluster in the middle. To further specify the voting
blocs we subset this large cluster, re-create a network, a sub-network,
and use again a community detection algorithm to identify clusters.

\begin{figure}
\includegraphics[width=1\linewidth]{../analysis/graphs/network/full-network} \caption{Network of voting clusters (10 year period 2004-2013)}\label{fig:unnamed-chunk-48}
\end{figure}

\hypertarget{list-of-communities}{%
\subsection{List of communities}\label{list-of-communities}}

\begin{table}
\caption{\label{tab:unnamed-chunk-49}Comminuty Membership I}

\centering
\fontsize{8}{10}\selectfont
\begin{tabular}[t]{lrr}
\toprule
Country & Cluster & Ties\\
\midrule
Afghanistan & 1 & 10\\
Bangladesh & 1 & 18\\
Burkina Faso & 1 & 12\\
Ghana & 1 & 15\\
Indonesia & 1 & 4\\
\addlinespace
Lao People's Democratic Republic & 1 & 7\\
Malaysia & 1 & 11\\
Namibia & 1 & 16\\
Pakistan & 1 & 12\\
Sri Lanka & 1 & 16\\
\addlinespace
Tanzania, United Republic of & 1 & 14\\
Uganda & 1 & 13\\
Zimbabwe & 1 & 12\\
Brunei Darussalam & 1 & 11\\
Australia & 2 & 9\\
\addlinespace
Austria & 2 & 15\\
Greece & 2 & 11\\
Ireland & 2 & 12\\
Japan & 2 & 7\\
New Zealand & 2 & 11\\
\addlinespace
Spain & 2 & 15\\
Sweden & 2 & 15\\
Turkey & 2 & 4\\
Liechtenstein & 2 & 6\\
Algeria & 3 & 16\\
\addlinespace
Bahrain & 3 & 17\\
Comoros & 3 & 6\\
Djibouti & 3 & 13\\
Egypt & 3 & 15\\
Gambia & 3 & 7\\
\addlinespace
Jordan & 3 & 11\\
\bottomrule
\end{tabular}
\centering
\begin{tabular}[t]{lrr}
\toprule
Country & Cluster & Ties\\
\midrule
Lebanon & 3 & 10\\
Morocco & 3 & 10\\
Oman & 3 & 16\\
Qatar & 3 & 15\\
Saudi Arabia & 3 & 15\\
\addlinespace
Tunisia & 3 & 17\\
United Arab Emirates & 3 & 17\\
Angola & 4 & 7\\
Benin & 4 & 14\\
Bhutan & 4 & 17\\
\addlinespace
Botswana & 4 & 20\\
Cameroon & 4 & 10\\
Cape Verde & 4 & 23\\
Colombia & 4 & 12\\
Congo & 4 & 11\\
\addlinespace
Côte d'Ivoire & 4 & 9\\
Eritrea & 4 & 8\\
Ethiopia & 4 & 18\\
Gabon & 4 & 12\\
Guinea & 4 & 13\\
\addlinespace
Guyana & 4 & 22\\
Haiti & 4 & 8\\
Kenya & 4 & 14\\
Kuwait & 4 & 13\\
Lesotho & 4 & 10\\
\addlinespace
Madagascar & 4 & 12\\
Mali & 4 & 17\\
Mexico & 4 & 13\\
Mozambique & 4 & 22\\
Nepal & 4 & 17\\
\bottomrule
\end{tabular}
\end{table}

\begin{table}
\caption{\label{tab:unnamed-chunk-49}Comminuty Membership II}

\centering
\fontsize{8}{10}\selectfont
\begin{tabular}[t]{lrr}
\toprule
Country & Cluster & Ties\\
\midrule
Philippines & 4 & 16\\
Rwanda & 4 & 8\\
Senegal & 4 & 15\\
Sierra Leone & 4 & 14\\
Suriname & 4 & 12\\
\addlinespace
Thailand & 4 & 11\\
Togo & 4 & 8\\
Trinidad and Tobago & 4 & 15\\
Venezuela, Bolivarian Republic of & 4 & 15\\
Zambia & 4 & 12\\
\addlinespace
Maldives & 4 & 16\\
Saint Lucia & 4 & 6\\
Argentina & 5 & 4\\
Armenia & 5 & 4\\
Ukraine & 5 & 6\\
\addlinespace
Azerbaijan & 6 & 5\\
Belarus & 6 & 4\\
Bolivia, Plurinational State of & 6 & 17\\
Brazil & 6 & 14\\
Chile & 6 & 16\\
\addlinespace
Costa Rica & 6 & 16\\
Ecuador & 6 & 15\\
El Salvador & 6 & 19\\
Fiji & 6 & 10\\
Guatemala & 6 & 12\\
\addlinespace
Honduras & 6 & 11\\
Jamaica & 6 & 13\\
Malawi & 6 & 8\\
Mauritius & 6 & 13\\
Nicaragua & 6 & 14\\
\addlinespace
Panama & 6 & 10\\
Paraguay & 6 & 14\\
\bottomrule
\end{tabular}
\centering
\begin{tabular}[t]{lrr}
\toprule
Country & Cluster & Ties\\
\midrule
Peru & 6 & 14\\
Singapore & 6 & 15\\
Solomon Islands & 6 & 6\\
Swaziland & 6 & 9\\
Timor-Leste & 6 & 10\\
\addlinespace
Uruguay & 6 & 12\\
Saint Vincent and the Grenadines & 6 & 5\\
Bahamas & 6 & 6\\
Barbados & 6 & 6\\
Belize & 6 & 4\\
\addlinespace
Belgium & 7 & 10\\
Bulgaria & 7 & 17\\
Canada & 7 & 9\\
Czech Republic & 7 & 9\\
Denmark & 7 & 21\\
\addlinespace
Estonia & 7 & 10\\
Finland & 7 & 22\\
Hungary & 7 & 13\\
Italy & 7 & 12\\
Latvia & 7 & 8\\
\addlinespace
Lithuania & 7 & 10\\
Luxembourg & 7 & 10\\
Netherlands & 7 & 10\\
Norway & 7 & 23\\
Poland & 7 & 22\\
\addlinespace
Portugal & 7 & 22\\
Romania & 7 & 17\\
Slovakia & 7 & 8\\
Iceland & 7 & 15\\
Iran, Islamic Republic of & 9 & 7\\
\addlinespace
Libya & 9 & 5\\
Syrian Arab Republic & 9 & 5\\
\bottomrule
\end{tabular}
\end{table}

\end{document}
